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In this article, the researchers investigated a deep texture analysis for the palmprint texture pattern representation based on a fusion between several texture information extractions through multiple descriptors, such as HOG and Gabor Filters, Fractal dimensions and GLCM corresponding respectively to the frequency, model, and statistical methodologies-based texture features. They assessed the proposed deep texture analysis method as well as the applicability of the dimensionality reduction techniques and the correlation concept between the features-based fusion on the challenging PolyU, CASIA and IIT-Delhi Palmprint databases. The experimental results show that the fusion of different texture types using the correlation concept for palmprint modality identification leads to promising results.<\/jats:p>","DOI":"10.4018\/ijdcf.2020040103","type":"journal-article","created":{"date-parts":[[2020,1,10]],"date-time":"2020-01-10T18:20:26Z","timestamp":1578680426000},"page":"40-58","source":"Crossref","is-referenced-by-count":3,"title":["Deep-Analysis of Palmprint Representation Based on Correlation Concept for Human Biometrics Identification"],"prefix":"10.4018","volume":"12","author":[{"given":"Raouia","family":"Mokni","sequence":"first","affiliation":[{"name":"College of Computer Engineering and Science, Sattam Prince Bin Abdulaziz University, Kharj, Saudi Arabia & Faculty of Economics and Management of Sfax, University of Sfax, Sfax, Tunisia"}]},{"given":"Hassen","family":"Drira","sequence":"additional","affiliation":[{"name":"IMT Lille Douai, Univ. 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